Artificial Intelligence in English Language Teaching: An Assessment of Teacher Comfort, Usage, Readiness, and Perceptions

Abstract

Artificial Intelligence (AI) is rapidly reshaping educational landscapes, yet its integration in developing contexts remains uneven due to significant infrastructural disparities. This study assessed the comfort, frequency of use, perceived usefulness, and readiness of K–12 English language teachers in the Philippines regarding AI integration. Grounded in the TPACK and SAMR frameworks, the study used a quantitative comparative research design, surveying 688 public school teachers from a highly urbanized division (Biñan City) and a predominantly rural division (Nueva Ecija). The findings reveal a distinct "readiness gap": teachers show high internal self-efficacy and comfort but report insufficient institutional support. While respondents frequently use AI for text-based administrative tasks, usage for transformative listening and speaking activities remains rare, indicating a stagnation at the "Substitution" level of technology integration. Statistical analysis confirms a significant "digital native" effect, where younger, early-career teachers exhibit greater adaptability than their seasoned peers. Crucially, a profound digital divide was identified, with urban teachers reporting significantly higher frequency of AI use (M = 3.98 vs. 3.25, t = 14.31, p < .001, d = 1.09) and readiness for integration (M = 4.05 vs. 3.41, t = 13.56, p < .001, d = 1.03) than their rural counterparts. The study also uncovered a pedagogical paradox wherein teachers acknowledge AI's utility but fear it diminishes students' critical thinking skills. These results challenge the efficacy of uniform training mandates, suggesting that sustainable integration requires differentiated strategies that prioritize infrastructural enablement in rural areas while focusing on ethical, pedagogical governance in urban settings. This research contributes to the literature by quantifying the impact of locational privilege on AI adoption and advocating context-sensitive policies to prevent deepening educational inequities.